WasteAssistant：用于智能垃圾分类与可持续管理的监管引导视觉问答框架
WasteAssistant: Regulation-Guided Visual Question Answering Framework for Intelligent Waste Segregation and Sustainable Managemen
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中文总结 AI 辅助
该研究针对现有智能垃圾分类系统的不足，提出集成视觉语言模型和多模态大语言模型的框架，构建新数据集，经实验验证基于BLIP的模型效果更佳，提升了分类准确率，确保监管合规，推动多模态AI在垃圾管理中的应用。
中文摘要 AI 辅助
高效的垃圾分类对可持续城市管理和环境治理至关重要。现有自动化系统受单模态视觉处理、上下文理解不足和监管一致性弱的限制。为解决这些问题，我们提出一个语言引导的视觉人工智能框架，集成视觉语言模型和多模态大语言模型进行联合视觉语言推理。该框架实现了符合印度2016年固体废物管理规则的视觉问答范式。我们构建了一个新的WasteVQA数据集，包含21个垃圾类别的13500个问答对。实验表明，基于BLIP的模型BLEU分数为0.8291，BERTScore为0.9273，优于传统基于CNN的方法。这项工作提高了源头分类准确率，确保监管合规，并支持面向市政和市民的垃圾管理的可扩展部署，促进多模态人工智能在可持续城市基础设施中的应用。
英文摘要
Efficient waste segregation is critical for sustainable urban management and environmental governance. Existing automated systems are limited by single-modality visual processing, insufficient contextual understanding, and weak regulatory alignment. To address these issues, we propose a language-guided vision-AI framework that integrates vision-language models and multimodal large language models for joint visual-linguistic reasoning. This framework implements a visual question answering paradigm aligned with India's Solid Waste Management Rules 2016. We construct a new WasteVQA dataset with 13,500 question-answer pairs across 21 waste categories. Experiments show that the BLIP-based model achieves a BLEU score of 0.8291 and a BERTScore of 0.9273, outperforming traditional CNN-based methods. This work improves source-level segregation accuracy, ensures regulatory compliance, and supports scalable deployment for municipal and citizen-facing waste management, promoting multimodal AI in sustainable urban infrastructure. The source code and dataset are available at: https://github.com/Khushkataruka/WasteAssistant
发表机构
- SVNIT（萨达尔·瓦拉巴伊·国家理工学院）
- NTNU（挪威科技大学）
- KIIT（卡林加工业技术学院）
机构由 AI 辅助整理，请以论文原文为准。
